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The effects of customer equity drivers on loyalty across services industries and firmsOu, Yi-Chun; Verhoef, Pieter; Wiesel, Thorsten

Published in:Journal of the Academy of Marketing Science

DOI:10.1007/s11747-016-0477-6

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Citation for published version (APA):Ou, Y-C., Verhoef, P. C., & Wiesel, T. (2017). The effects of customer equity drivers on loyalty acrossservices industries and firms. Journal of the Academy of Marketing Science, 45(3), 336-356. DOI:10.1007/s11747-016-0477-6

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ORIGINAL EMPIRICAL RESEARCH

The effects of customer equity drivers on loyalty across servicesindustries and firms

Yi-Chun Ou1& Peter C. Verhoef2 & Thorsten Wiesel3,4

Received: 26 March 2015 /Accepted: 17 February 2016 /Published online: 11 March 2016# The Author(s) 2016. This article is published with open access at Springerlink.com

Abstract Customer equity drivers (CEDs)—value equity,brand equity, and relationship equity—positively affect loyal-ty intentions, but this effect varies across industries and firms.We empirically examine potential industry and firm character-istics that explain why the CEDs–loyalty link varies acrossservices industries and firms in the Netherlands. The resultsshow that (1) some previously assumed industry and firmcharacteristics have moderating effects while others do notand (2) firm-level advertising expenditures constitute the mostcrucial moderator because they influence all three loyaltystrategies (significant for value equity and brand equity; mar-ginally significant for relationship equity), while three indus-try contexts (i.e., innovative markets, visibility to others, and

complexity of purchase decisions) each influence two of thethree loyalty strategies. Our results clearly show that specificindustry and firm characteristics affect the effectiveness ofspecific loyalty strategies.

Keywords Customer loyalty . Customer relationshipmanagement . Customer equity . Brand equity . Multi-levelanalysis

Introduction

With markets becoming increasingly competitive, firms aredevoting considerable attention to the issue of loyal cus-tomers—specifically, how to attain them and maximize theirvalue (e.g., Watson et al. 2015). Among the many studies thathave tried to better understand customer loyalty, one of themost influential is the study of Rust et al. (2000) and theircustomer equity model. Their follow-up article (i.e., Rustet al. 2004) further solidifies the value of said model amongacademics. The Rust et al. (2000) model proposes that threecustomer equity drivers (CEDs) are crucial components ofloyalty intentions: value equity (VE), brand equity (BE), andrelationship equity (RE).1 In turn, research has provided con-vincing support for the proposed impact of CEDs; for

1 In this study, Bloyalty intentions^ refer to customers’ self-reported prob-abilities of repurchasing from competing firms within an industry, i.e.,measuring the loyalty shares among the competing firms (Rust et al.2004). VE is defined as customers’ objective assessment of what is givenup for what is received; BE is customers’ subjective assessment of brandimage; RE is customers’ overall assessment of their interaction qualitywith firms (Lemon et al. 2001; Rust et al. 2004). Rust et al. (2000) labelRE as retention equity, but later work re-labels this concept as relationshipequity to better reflect the substantive concept.

Mark Houston served as area editor for this article.

Electronic supplementary material The online version of this article(doi:10.1007/s11747-016-0477-6) contains supplementary material,which is available to authorized users.

* Yi-Chun [email protected]

Peter C. [email protected]

Thorsten [email protected]

1 Marketing Division, University of Leeds, Leeds LS2 9JT, UK2 Department of Marketing, Faculty of Economics and Business,

University of Groningen, P.O. Box 800, 9700AV Groningen, The Netherlands

3 Marketing Center Münster, Westfälische Wilhelms-UniversitätMünster, Am Stadtgraben 13-15, 48143 Münster, Germany

4 Department of Marketing, Faculty of Business and Economics,University of Groningen, Groningen, The Netherlands

J. of the Acad. Mark. Sci. (2017) 45:336–356DOI 10.1007/s11747-016-0477-6

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example, Ou et al. (2014) generalize this positive link to 13services industries.

Despite the positive support for a CEDs–loyalty link, stud-ies have widely indicated that loyalty strategies are not equallyeffective across industries and firms (e.g., DeHaan et al. 2015;Eisenbeiss et al. 2014; Kumar et al. 2013; Rust et al. 2004). Ouet al. (2014) empirically show that there are substantial varia-tions in the effects of CEDs across industries and firms.However, explanations for cross-industry and cross-firm var-iations remain scarce. As Table 1 shows, multiple studies haveinvestigated the cross-customer variation in the effectivenessof customer loyalty determinants (i.e., satisfaction, commit-ment, and trust). They examine, for example, demographics,relationship length, switching costs, and consumer confidenceas customer-level moderators to explain the variation (e.g.,Mittal and Kamakura 2001; Ou et al. 2014). Studies have alsoinvestigated industry-level (e.g., Seiders et al. 2005) or firm-level (i.e., Verhoef et al. 2007) moderators. Overall, however,few industry- and firm-level moderators have been systemat-ically identified and empirically examined. This constitutes aresearch gap in the customer relationship management (CRM)literature. Examining this gap is crucial because situationaltheory indicates that identifying the contexts relevant to indi-vidual reactions is as important as understanding individualcognitive processes; it also assumes that customers in differentcontexts employ different comparison standards in decision

making (Eisenbeiss et al. 2014; Longshore and Prager 1985)Thus, our main objective is to develop a framework of indus-try and firm characteristics as moderators and empirically ex-amine the extent to which they explain cross-industry andcross-firm variations on the link between CEDs and loyaltyintentions.

Rust et al. (2000) initially discussed some industry charac-teristics asmoderators. They argued that VE is more importantfor homogeneous markets, BE for visible goods/services, andRE for contractual settings. Building on their discussion andthe CRM literature, we derive multiple important industry-and firm-level moderators and theorize their impact on the linkbetween CEDs and loyalty. In the following section, we fur-ther detail how we select these moderators and formulate thehypotheses. Next, we use three data sources to examine theimpact of these moderators: (1) a large-scale customer dataset(including 8924 customer responses of 95 leading companiesacross 18 services industries in the Netherlands), (2) an expertsurvey consisting of 178 responses from 88 managers andbusiness consultants, and (3) external sources, including datafrom ACNielsen on firms’ annual advertising expendituresand from firms’ annual revenue reports. We use a multi-levelmodel with four levels to estimate the proposed moderatingeffects.

Overall, this study provides three contributions to the ex-tant literature. The first contribution pertains to a research gap

Table 1 Prior empirical studies regarding the moderators on the effects of CEDs

Studies Main effects Moderators

VE BE RE Customercharacteristics

Firmcharacteristics

Industrycharacteristics

Countrycharacteristics

Mittal and Kamakura (2001) ✓ ✓

Verhoef et al. (2002) ✓ ✓ ✓

Nijssen et al. (2003) ✓ ✓ ✓

Gustafsson et al. (2005) ✓ ✓ ✓

Seiders et al. (2005) ✓ ✓ ✓

Bell et al. (2005) ✓ ✓

Cooil et al. (2007) ✓ ✓

Chandrashekaran et al. (2007) ✓ ✓

Verhoef et al. (2007) ✓ ✓ ✓ ✓

Voss et al. (2010) ✓ ✓ ✓

Eisenbeiss et al. (2014) ✓ ✓ ✓

Frank et al. (2014) ✓ ✓ ✓ ✓ ✓ ✓

Nagengast et al. (2014) ✓ ✓

Ou et al. (2014) ✓ ✓ ✓ ✓

Keiningham et al. (2015) ✓ ✓

Summary of previous studies 14/15a 3/15a 7/15a 12/15a 2/15a 5/15a 1/15a

Current study ✓ ✓ ✓ ✓ ✓ ✓ ×

a In these fractions, the denominator refers to the number of previous studies included in Table 1; the numerator refers to the number of topics studied inthe previous studies. Take VE as a main effect for illustration. Fourteen out of fourteen studies have examined VE as a main effect

J. of the Acad. Mark. Sci. (2017) 45:336–356 337

in theorizing and testing the contextual moderators in CRM.Specifically, we theoretically explain cross-industry andcross-firm variations of the effectiveness of CEDs by identi-fying a framework of multiple industry and firm characteris-tics. Consequently, we empirically show that the effects ofCEDs are indeed influenced by some theoretically assumedindustry and firm characteristics. For example, the effect ofRE decreases in innovative markets and for heavy advertisers.This is a significant contribution in the CRM literature, whichcurrently lacks a systematic investigation of industry- andfirm-level moderators’ impacts (Kumar et al. 2013; Rustet al. 2004).

The second contribution is on the methodological level.Weuse multiple data sources, including customer data, expertdata, and secondary data, which allow us to explore how cus-tomers react to different contexts and to achieve a better un-derstanding of how these reactions ultimately influence therole of CEDs on customers’ loyalty decisions in specific con-texts. We also control for customer-level moderators whileinvestigating the joint moderating effects of industry- andfirm-level characteristics on the link between CEDs and loy-alty intentions. To our knowledge, no studies have simulta-neously included all three levels as moderators. This is a crit-ical gap because prior research has indicated that customers’decision making is influenced by their personal characteristicsand also their economic system (Johns 2006; Molloy et al.2010).

The third contribution is managerial. Our findings helpmanagers across different services industries determine whichindustry and firm characteristics actually have a contingentimpact and which loyalty strategies are most/least effectivein a specific context. At the industry level, we provide moreinsight into how firms should adapt CEDs to their industryenvironment to enhance loyalty intentions. For example,while firms are able to use RE to increase loyalty intentionsby 15% on average, this effect decreases to 7.6% in innovativemarkets. At the firm level, we provide more insight into howfirms should effectively combine CEDs and their specific firmcharacteristics. For example, the average effect (15%) of usingRE decreases to 11.4% for heavy advertisers. A study thatclosely parallels our own is that of Ou et al. (2014), thoughour work differs in several ways. Specifically, we have a dif-ferent aim (moderating role of industry and firm characteris-tics vs. moderating role of consumer confidence only), simul-taneously incorporate moderators at several levels (multipleindustry-, firm- and customer-level moderators vs. only onecustomer-level moderator), and apply multiple datasets. Wealso adopt a new consumer survey, an expert survey, and ex-ternal sources and provide more nuanced insights relevant formanagers.2

Theoretical background and hypotheses

Rust et al.’s (2000) customer equity model identifies (1) valueequity, (2) brand equity, and (3) relationship equity as threedrivers of loyalty intentions.We adopt the definitions of CEDsfrom Lemon et al. (2001) and Rust et al. (2004). VE capturescustomers’ objective assessments of the utility of goods/services based on perceptions of Bwhat is given up^ for Bwhatis received.^ VE reflects the outcome of customers’ compar-isons between their own expectations and firms’ performance.Expectation–disconfirmation theory (e.g., Homburg et al.2006) argues that customers are more satisfied with a firm’sofferings when they perceive high VE, which leads to higherloyalty intentions. BE refers to customers’ subjective and in-tangible assessments of the brand image. We view brand im-age as brand strength and brand innovativeness in this study,as both are main criteria of adding value to brands (BrandZ2015; Rooney 2014) and are important drivers of customer-based BE (e.g., Mizik and Jacobson 2003). Theoretically, theeffect of BE on loyalty intentions could be well explained bysignal credibility and the best Bfit.^3 Signal credibility ofstrong brands reduces the perceived performance risk ofgoods/services (Erdem et al. 2006), thereby inducing higherloyalty intentions. The best Bfit^ (Rust et al. 2000) explainsthat customers tend to prefer a brand when they perceive a fitbetween brand image and their self-image and personality(e.g., status and social identity). Last, RE captures customers’overall assessments of their interaction quality with the firm.On the basis of reciprocity, customers perceiving good rela-tionships with firms tend to care about firms’ welfare and toavoid any decision that might damage firms (Aurier andN’Goala 2010; Selnes and Gønhaug 2000). As a result, ifperceived RE is high, customers feel a strong connection withthe firm, which has a positive effect on loyalty intentions.

Although CEDs may positively affect loyalty intentions,Rust et al. (2000) suggest that the effects may strongly differbetween industries. Ou et al. (2014) confirm strong variationsof the effects of CEDs across industries; they also reveal var-iations in these effects between firms. Studies (e.g., Kumaret al. 2013) have also acknowledged the differential effectsof customer attitudes (i.e., satisfaction) across industries andfirms. According to situational theory, customers in differentcontexts tend to employ different comparison standards indecision making (Eisenbeiss et al. 2014; Longshore andPrager 1985). Each industry/firm has its own characteristicsthat customers commonly evaluate (Mauri and Michaels1998). As such, customers within an industry/a firm may be-have similarly. Unique industry/firm characteristics shape cus-tomers’ preferences and decision patterns. Situational theory

2 In Web Appendix A, we further outline the similarities and differencesbetween the current study and Ou et al. (2014).

3 This study uses signal credibility and the best Bfit^ to explain the mech-anisms by which BE influences loyalty intentions. We do not empiricallytest these two mechanisms of BE.

338 J. of the Acad. Mark. Sci. (2017) 45:336–356

also indicates that identifying the contexts relevant to individ-ual reactions is as important as understanding individuals’cognitive processes. Thus, it is necessary to derive a robustframework of important industry and firm characteristics toexamine the differential effectiveness of CEDs in specific con-texts. In the following, we discuss how we selected industryand firm characteristics.

Moderator selection and conceptual model

Becausemany potential industry and firm characteristics exist,we used four steps to select these characteristics for the pur-pose of explaining the concerned cross-industry and cross-firm variations. The selection is based on Rust et al.’s (2000)proposed moderator framework and available knowledge onindustry/firm moderators in the CRM literature (see Table 2).

In the initial step, we critically evaluated Rust et al.’s(2000) presumed moderators and their testability. First, cus-tomer involvement is more of a customer- than an industry-level moderator and has already been tested in prior studies(e.g., Homburg and Giering 2001). Thus, we included

customer involvement in our model as a customer-level con-trol variable. Second, some moderators tend to be relevant toRE. For example, as Rust et al. (2000) argue that customersreceive large benefits from RE, the implication then followsthat firms find wisdom in steering RE and providing largebenefits (i.e., through loyalty programs; Dorotic et al. 2012)to maintain loyalty. Furthermore, a strong brand communitybetween customers tends to involve customer commitment(Bagozzi and Dholakia 2006), which is one component ofRE (Rust et al. 2000). Therefore, we do not include thesetwo suggested moderators in the model. Third, some moder-ators are rather difficult to test and/or are not applicable inconsumer services industries (e.g., business-to-business[B2B] settings), and our focus is on consumer markets. Forexample, the importance of recycling seemsmore important inproduct industries. This issue is also common among inter-generational brands (e.g., cars). Thus, we initially selectedseven of Rust et al.’s (2000) presumed moderators: (1) thepresence of differences between competitors, (2) the impor-tance of customer learning, (3) the difficulty of evaluatingquality prior to consumption, (4) innovative markets, (5)

Table 2 Steps of selecting industry- and firm-level moderators

Moderators Step 1 Step 2 Step 3 Step 4

Industry-level moderators from Rust et al. (2000)

Customer involvement Excluded: included as a customer-level variable

– – –

Benefits associating with loyaltyprograms

Excluded: tend to be relevant toRE

– – –

Strong communities associating withgoods/ services

Excluded: tend to be relevant toRE

– – –

B2B settings Excluded: not our focus – – –

Recycling of products in the maturestage

Excluded: more important inproduct industries

– – –

Inter-generational brands Excluded: more important inproduct industries

– – –

Presence of differences betweencompetitors

Included Included Excluded: a characteristic of competitive intensity –

Importance of customer learning Included Included Excluded: related to difficulty of evaluating qualityprior to consumption

–

Difficulty of evaluating quality priorto consumption

Included Included As a control variable because we aim at existingcustomers

–

Innovative markets Included Included Included Included

Contractual settings Included Included Included Included

Visibility to others Included Included Included Included

Complexity of purchase decisions Included Included Included Included

Industry-level moderators from the CRM literature

Competitive intensity – Included Included Included

Market dynamics – Included Excluded: related to innovative markets –

Firm-level moderators

Market position – – – Included

Advertising expenditures – – – Included

-: not considered

Bold: the moderators chosen for this manuscript


contractual settings, (6) visibility to others, and (7) the com-plexity of purchase decisions.

In the second step, we considered the existing literature onCRM. We mainly found studies that discuss the moderatingrole of competitive intensity and market dynamics (e.g.,Seiders et al. 2005; Voss et al. 2010), which are also included.In the third step, we critically evaluated the resulting nineindustry-level moderators to determine any theoretical overlapamong them (Evans 1991). For example, competitive intensi-ty and the presence of differences between competitors arestrongly related, as competition is usually more intense inindustries with homogeneous goods/services (Menguc andAuh 2006). As competitive intensity is a frequently studiedand well-established industry moderator, we focused on thatfactor. Next, the difficulty of evaluating quality prior to con-sumption and the importance of customer learning are related.If customers face problems with evaluating goods/services,they eventually learn about the performance of these goods/services during consumption. Given this theoretical overlap,we focused on the difficulty of evaluating quality prior toconsumption, as this concept is more clearly discussed in theliterature (e.g., Fischer et al. 2010). However, this moderatormay have less impact on existing customers, who are the mainrespondents of this study. Therefore, we used the moderator asa control variable. Finally, we expect that innovative marketsand market dynamics are two sides of the same coin: firms indynamic markets rapidly introduce innovative goods/servicesto meet sudden changes in customer demand (Slater andNarver 1994). In other words, rapidly introducing innovativegoods/services is a phenomenon of innovative markets. Thus,we chose to focus on innovative markets, as this moderator isdiscussed more heavily in Rust et al.’s (2000) framework. Insummary, we chose to investigate five industry moderators:(1) innovative markets, (2) contractual settings, (3) visibilityto others, (4) complexity of purchase decisions, and (5) com-petitive intensity.

Researchers maintain that contexts at the meso- and micro-levels play a role in influencing the relationships we examine(Bamberger 2008). In addition to industry characteristics, pri-or research has uncovered heterogeneity between firms re-garding the effect sizes of CEDs on loyalty intentions (Ouet al. 2014). In the fourth step, given industry characteristicsas the meso-level context influencing customer perceptions ofloyalty strategies, we assume that firm characteristics lie at themicro-level; therefore, we also included firm characteristics inour framework. We chose to include two firm characteristicsas crucial marketing variables: (1) market position and (2)firm-level advertising expenditures. Many firms strive to bemarket leaders, while they also use advertising to differentiatethemselves from competitors (e.g., Fischer et al. 2016).

Regarding market position, research assumes that marketleaders have more advantages over followers. For example,when market leaders promote goods/services, they have a

greater influence on competitors/followers and draw fol-lowers’ market share. In turn, followers’ promotions do noteasily influence leaders’ market share (Hoeffler and Keller2003). However, there is a strong debate within CRM aboutwhether brands with a high market share can actually benefitfrom loyalty-based strategies (e.g., Dowling and Uncles 1997;Ehrenberg et al. 1990). As a result, how to choose effectivecontext-specific loyalty strategies is still an unresolved puzzlefor both market leaders and followers.

Regarding firm-level advertising expenditures, marketpower theory argues that heavy advertisers are able to improvebrand recognition and reputation to charge higher prices andmaintain a given scale of sales (e.g., Kaul and Wittink 1995),assuming a strengthened effect of BE and weakened effect ofVE for heavy advertisers. Despite BE and VE, customer rela-tionships are crucial for building strong ties for services indus-tries (e.g., Mende et al. 2013). However, little is known abouthow firm-level advertising expenditures might influence theeffect of CRM. As a result, because decisions about theamount of money to spend on advertising influence firm per-formance (Fischer et al. 2016), we take an initial step of em-pirically examining the interaction effects of firm-level adver-tising expenditures and CEDs on loyalty intentions.

Figure 1 depicts the resulting conceptual model. As themodel shows, CEDs are positively related to loyalty inten-tions; however, five industry characteristics and two firm char-acteristics moderate these relationships. To account for poten-tial omitted variable bias, we included the main effects andalso the moderation effects of several control variables.

Next, we hypothesize the impact of the industry and firmcharacteristics on the link between CEDs and loyalty inten-tions. Drawing on multiple theories in the CRM literature, wedevelop the hypotheses by exploring how customers react tothese contexts and theorizing how these reactions, in turn,influence the role of CEDs on customers’ loyalty decisionsin specific contexts.

We did not hypothesize the moderating impact of the in-dustry and firm characteristics on all CEDs, as some impactsare rarely theorized or mentioned in the CRM literature.Having no strong underlying theories or empirical findingsmay give uncertain directions of the moderating impact. Assuch, we left some moderating impacts open as an empiricalquestion. For example, prior studies have mainly found aweakened effect of VE in competitive industries and paid littleattention to the impact of competitive intensity on the effectsof BE and RE (e.g., Seiders et al. 2005; Voss et al. 2010).Thus, we have little idea of how competitive intensity moder-ates the effects of BE and RE. On the one hand, we mightsurmise that BE and RE are more important in competitiveindustries, as both are difficult to imitate and should be impor-tant differentiators for firms in competitive industries (Rustet al. 2000). On the other hand, we might assume that BEand RE are less important in competitive industries, as


competitors intensively react to and imitate each other’s mar-keting strategies (Gatignon 1984), implying that the strengthof BE and RE as differentiators could decrease.

Similarly, while there is sufficient evidence of the mod-erating impact of innovative markets on the effects of VEand BE, we do not know how innovative markets moderatethe effect of RE on loyalty intentions. On the one hand, theeffect of RE might be strengthened in innovative markets.Customers perceiving high RE are likely to trust firms,which decreases customer uncertainty of the goods/services performance in innovative markets (Palmatieret al. 2009). On the other hand, the effect of RE might beweakened in innovative markets. Customers in innovativemarkets constantly search for new services and tend to paymore attention to value rather than their relationships withthe firm (De Luca and Atuahene-Gima 2007). In addition,regarding contractual settings, in their framework, Rustet al. (2000) mention its moderating impact only on RE,not on VE or BE.

Industry characteristics

Competitive intensity Competition is usually more intense inindustries with homogeneous rather than heterogeneousgoods/services (Menguc and Auh 2006). When the offeringsof goods/services are virtually the same, VE is difficult to

build and becomes the basic requirement of all firms operatingin competitive industries (Reimann et al. 2010; Rust et al.2000). As a result, firms may be less likely to benefit fromimplementing VE because the perceived differences in VE areso tiny that customers can hardly use VE to differentiate be-tween firms. Customers thus may pay less attention to VEwhen they make loyalty decisions, implying that the effectof VE is weaker in competitive industries (e.g., Seiders et al.2005). We thus formulate the following hypothesis for VE:

H1ve: The relationship between VE and loyalty intentions isweaker in more competitive industries than in lesscompetitive industries.

Innovative markets Innovative markets tend to have morenew ideas, a higher level of innovative activities, and a largeramount of R&D expenditures than do less innovative markets(Pleatsikas and Teece 2001). As a consequence, firms in inno-vative markets frequently introduce new goods/services to themarket (Pleatsikas and Teece 2001) to improve customers’lives through better quality, usefulness, and ease (e.g.,Hauser et al. 2006)—all components of VE. Customers ininnovative markets thus expect better value from new goods/services, implying that VE is a critical criterion in particularfor these customers in their decisions to purchase new goods/

Firm Characteristics

Market position (Exploration)Advertising expenditures (H6ve: -, H6be: +) Customer Characteristics

Gender Age

Income Relationship length

Switching costs Customer involvement Consumer confidence

Industry Characteristics Difficulty of evaluating

quality prior to consumption

CEDs

Value equity (VE) Brand equity (BE)

Relationship equity (RE)

Loyalty Intentions

Industry Characteristics

Competitive intensity (H1ve: -)Innovative markets (H2ve: +, H2be: +)

Contractual settings (H3re: +)Visibility to others (H4ve: +, H4be: +, H4re: -)

Complexity of purchase decisions (H5ve: +, H5be: +, H5re: +)

Fig. 1 Conceptual model


services. As such, we expect that VE is more important forcustomers in innovative markets.

Regarding BE, customers perceive uncertainty and risks inpurchasing new goods/services (Littler andMelanthiou 2006).For example, customers are uncertain about information re-garding new goods/services and choosing among alternatives(Urbany et al. 1989). BE thus might be more important ininnovative markets. Erdem and Swait (1998) indicate thatBE, such as strong and innovative brands, functions as a cred-ibility signal, reducing information search costs and perceivedrisks in innovative markets. Together, we expect that VE andBE are more important for customers in innovative markets.

H2ve: The relationship between VE and loyalty intentions isstronger in more innovative markets than in less inno-vative markets.

H2be: The relationship between BE and loyalty intentions isstronger in more innovative markets than in less inno-vative markets.

Contractual settings In the CRM literature, the distinctionbetween contractual and non-contractual settings is deemedimportant because customers may behave differently if theyare contractually bound to a specific firm (Fader and Hardie2007). In a contractual setting, customers and firms have anagreement on terms and conditions stated in the contract andthe agreement is valid for a period of time (Gulati 1995).Customers in contractual settings tend to be locked in duringthe contractual period, but customers in non-contractual set-tings have the freedom to buy goods/services simultaneouslyfrom multiple firms (Burnham et al. 2003; Wirtz et al. 2014).

Because of the assumed behavioral difference between con-tractual and non-contractual settings, in accordance with Rustet al. (2000), we expect that RE is amore important strategy forfirms in contractual than in non-contractual settings. RE is away to Bglue^ customers to the firm. Contractual relationshipslock customers in, providing the firm with more opportunitiesto glue existing customers to the firm because the firm has adirect connection with customers and thus knows exactly whothey are and can collect more information about them. Forexample, firms may provide specific and personalized offer-ings (e.g., loyalty programs, affinity programs, special recog-nition and treatment, community-building programs,knowledge-building programs). Customers thus perceive theuniqueness of RE provided by contractual firms, which helpsfirms create unique relationships with customers (Bowen andJones 1986) and increases customer attachment/commitmentto postpone the termination of the contract (Rust et al. 2000).Evidence indicates that specific relationship constructs, such ascommitment, have an effect on loyalty, particularly in contrac-tual settings (e.g., Verhoef 2003). Compared with contractualsettings, firms in non-contractual settings face more problems

in identifying customers and collecting sufficient informationabout them. Consequently, these relationship-building strate-gies are less effective in non-contractual settings (e.g., Dowlingand Uncles 1997). In summary, we expect that RE is a strongerstrategy to enhance loyalty intentions in contractual than non-contractual settings.

H3re: The relationship between RE and loyalty intentions isstronger in contractual than in non-contractual settings.

Visibility to others Visibility to others is the extent to whichothers notice customers’ use of goods/services (Fisher andPrice 1992). Social comparison cue theory proposes that whenpeople notice social comparison cues (e.g., visible goods/ser-vices), their public self-consciousness tends to be high, andthus they are concerned about what others think of them(Bearden and Rose 1990; Fisher and Price 1992). If so, cus-tomers are likely to pay more attention to publicly noticeableattributes/elements of visible goods/services, as other people’sperceptions of what one uses or buys is important in thiscontext. This indicates that if CEDs are perceived as beingpublicly noticeable, they should become more important forcustomers and, thus, for their loyalty intentions.

Specifically, VE is customers’ objective assessments of thevalue of goods/services (Rust et al. 2000). The OxfordLearner’s Dictionary defines objectivity as Bthe fact of notbeing influenced by personal feelings or opinions but consideronly facts that can be proved,^ which implies that VE tends tobe assessed by facts presented (e.g., quality and prices of thegoods/services). This indicates that the value of goods/services is commonly shared, meaning that the majority ofother customers can easily recognize the value; as such, VEtends to be publicly noticeable. Consequently, the notion ofpublic recognition implies that customers may pay more at-tention to VE in visible goods/services.

BE is even more publicly noticeable than VE, actingas a symbol of social status and identity (Rust et al.2000). To maintain or indicate their perceived statusand identity, customers are concerned about how othersthink of which brand they use or buy. As a result, cus-tomers tend to consider BE more relevant in decisionmaking when the usage of goods/services is more visible(Fischer et al. 2010; Rust et al. 2000). This behavior isrooted in the need to make a good impression (Beardenand Rose 1990) and to be accepted by desired referencegroups (Bearden and Etzel 1982).

Finally, RE is less likely to be publicly noticeable than VEand BE because the interaction between customers and thefirm is embedded in customers’ minds (Aurier and N’Goala2010). For example, some stores warmly greet customers oractively provide help from frontline staff as tools for buildingcustomer relationships. Loyalty programs are also a popular


customer relationship tool, providing customers useful cus-tomized coupons or special offers. The interactions with thefrontline staff in the stores are meant to improve customerattachment; the offers provided by the loyalty program aremeant to inform customers that stores understand their needs.Customer attachment or knowledge of needs is likely to beexperienced and recognized by customers themselves only, asneither aspect can be easily observed bymany other customersand thus is less likely to be used for social comparison pur-poses. Thus, we expect the following:

H4ve: The relationship between VE and loyalty intentions isstronger for visible than for less visible services.

H4be: The relationship between BE and loyalty intentions isstronger for visible than for less visible services.

H4re: The relationship between RE and loyalty intentions isweaker for visible than for less visible services.

Complexity of purchase decisions The complexity of pur-chase decisions is characterized by an ongoing and motivatedcognitive process in which multiple important sources of in-formation are integrated to produce an outcome (Wood andBandura 1989). That is, customers undergo a more elaboratedecision process, which may lead them to evaluate the Bwholepackage^ of the offering more intensively. If so, we expectthat all three CEDs are crucial for customers in the context ofpurchase decision complexity, as CEDs are relevant to most ofthe important firm characteristics perceived by customers andmay help customers reduce the complexity of purchase deci-sions (Rust et al. 2004; Vogel et al. 2008).

Specifically, Rust et al. (2000) propose that VE is moreimportant for customers when purchase decisions are com-plex, as customers tend to evaluate the components (i.e.,price, quality, and convenience) of VE carefully to derivethe most utility. Regarding BE, customers encounteringcomplex purchase decisions spend more time in collectinginformation to decrease risks of the future performance ofgoods/services (Wood and Bandura 1989). BE providessignal credibility, which may help customers reduce thecomplexity of purchase decisions, as credibility decreasessearch costs and also guarantees the quality of goods/services (e.g., Erdem et al. 2006). Last, RE creates trustin firms (Rust et al. 2000), which strengthens customers’beliefs in firms’ beneficent behavior in the future (Aurierand N’Goala 2010) and thus may also increase the impor-tance of RE in complex purchase decisions. Thus, we for-mulate the following hypotheses:

H5ve: The relationship between VE and loyalty intentions isstronger for complex purchase decisions than for lesscomplex purchase decisions.

H5be: The relationship between BE and loyalty intentions isstronger for complex purchase decisions than for lesscomplex purchase decisions.

H5re: The relationship between RE and loyalty intentions isstronger for complex purchase decisions than for lesscomplex purchase decisions.

Firm characteristics

Market positionWe use the term market position to examinehow market leaders and followers differ in terms of the effec-tiveness of CEDs on loyalty intentions. Market leaders outsellmarket followers and have the largest percentage of marketshare in the corresponding industries (Hoeffler and Keller2003).We empirically explore the moderation effect of marketposition, rather than formulating hypotheses, as double jeop-ardy theory (e.g., Ehrenberg et al. 1990) and information ac-cessibility theory (Feldman and Lynch 1988; Knapp et al.2014) propose opposite moderation effects of market position.The former proposes that CEDs are less important for marketleaders, and the latter proposes that they are more important.

Specifically, in the customer loyalty literature, research hasdiscussed the link between market share and customer loyalty(e.g., Ehrenberg et al. 1990). Researchers have empiricallydemonstrated the existence of the double jeopardy phenome-non: The key idea is that market leaders (market followers)have more (fewer) buyers who are also more (less) loyal(Ehrenberg et al. 1990). One implication of this empiricalregularity is that market leaders should put low expectationson their marketing programs’ ability to influence loyalty in-tentions because their customers already have strong loyaltyintentions. In other words, market leaders are more likely toencounter the ceiling effect than market followers wheninvesting in marketing strategies. As a result, double jeopardytheory proposes that the CEDs–loyalty link becomes weakerfor market leaders.

In contrast, information accessibility theory proposes thatthe CEDs–loyalty link is stronger for market leaders than formarket followers. This theory argues that the ability to accessinformation is crucial for customers as input to judgment anddecisions (Feldman and Lynch 1988; Knapp et al. 2014). Oneadvantage of being a market leader is that customers tend tohave more knowledge of market leaders than followers(Hoeffler and Keller 2003). More knowledge increases infor-mation accessibility, and thus customers have better encodingability to retrieve relevant firms’ information (Keller 1993).Moreover, knowledge increases customers’ confidence inevaluating goods/services and willingness to translate re-trieved information into decision making (Park et al. 2010).

Advertising expenditures BAdvertising^ here refers to non-price advertising, which excludes price discounts (Kaul and


Wittink 1995). We expect that for heavy advertisers, VE be-comes weaker while BE becomes stronger. In terms of VE,market power theory (Mitra and Lynch 1995) suggests thatadvertising decreases price sensitivity because advertising cre-ates differentiation, which enables firms to charge higherprices (Kaul and Wittink 1995). Because price is one compo-nent of VE, this theory implies that customers who are ex-posed to more advertising become less sensitive to perceivedVE and thus are less likely to translate perceived VE intoloyalty intentions. Consequently, firms with intensive expen-ditures may experience a weakened effect of VE.

In terms of BE, non-price advertising aims to establishhigh brand familiarity (Kaul and Wittink 1995), an extrin-sic cue that signals brands (Pieters and Wedel 2004). Mereexposure theory proposes that customers form brand pref-erences partly from familiarity triggered by advertising,which implies that the more customers are exposed toadvertising, the more familiar they are with brands, andthe more likely they are to consider those brands in theirdecision making (McAlister et al. 2007). Brand prefer-ences may be even more salient for firms with intensiveadvertising expenditures, as these firms provide a suffi-cient number of exposures to enhance the wear-in effectof advertising (Vakratsas and Ambler 1999). Thus, weassume that the effect of BE is strengthened for firms withintensive advertising expenditures.

We do not expect advertising to have a moderatingeffect on the RE–loyalty link, because RE is mainly builtover time when customers interact with firms throughloyalty programs, special recognition and treatment pro-grams, affinity programs, and community programs (Rustet al. 2000). Such programs are strategies of customerrelationship management and usually distinct from thefunctions of advertising. In summary, we only hypothe-size the moderating effect of advertising expenditures onthe VE–loyalty and BE–loyalty links:

H6ve: The relationship between VE and loyalty intentions isweaker for firms with higher advertising expendituresthan for firms with lower advertising expenditures.

H6be: The relationship between BE and loyalty intentions isstronger for firms with higher advertising expendituresthan for firms with lower advertising expenditures.

Research design

Data

To examine the heterogeneity of the effects of CEDs on loy-alty intentions at the industry and firm level, we used threetypes of data sources. The first data source is part of a large-

scale measurement of customer performance in theNetherlands. The data include 8924 customer responses of95 leading companies across 18 services industries (i.e., insur-ance, health insurance, banking, mobile phone, landlinephone, energy providers, gasoline providers, travel agencies,holiday resorts, airlines, supermarkets, health/beauty retailing,department stores, electronic retailing, do-it-yourself retailing,furnishing retailing, e-booking, and online retailing). For eachindustry, the survey provides respondents with a list of firms(between four and 11). Respondents can choose the compa-nies (maximum of three) they are currently a customer of andthen repeatedly answer questions about the chosen firms. Thesample size per industry was between 303 and 781 customers,with men comprising 46.4% of our collective sample. In termsof age, 22.9% of the respondents were between the ages of 18and 29 years, 24.8% were between 30 and 39 years, 20.1%were between 40 and 49 years, 25.3% were between 50 and64 years, and 7.0% were more than 65 years. In terms ofhousehold income, 48.9% of respondents fell into the categoryof €30,000 to €60,000 per year.

The second data source is an expert survey in which 88respondents (managers and business consultants) gave 178responses regarding their opinions on the studied industrycharacteristics. One respondent could provide multiple re-sponses to different industries. The managers came from theleading firms of multiple industries in the Netherlands (e.g.,Aegon, ING bank, KPN, Wehkamp, Ziggo). The businessconsultants included principals, consultants, and business an-alysts from leading consultancies in the Netherlands (e.g.,BCG, Deloitte, Ernst & Young, McKinsey). We e-mailed2000 questionnaires to the experts and informed them thatwe would donate €2 to the charity organizations of theirchoice upon completion of one questionnaire. As a result, 88experts provided 185 responses, seven of which were incom-plete. Therefore, the complete responses are 178, for a re-sponse rate of 4.4%. We provide additional information ofthe expert survey in Web Appendix B. The third data sourceconsists of external sources, including data from ACNielsenon firms’ annual advertising expenditures and from firms’annual revenue reports.

Measurement of variables

Measurement of loyalty intentions Following Rust et al.(2004), we measured loyalty intentions with self-reportedprobabilities of repurchasing. The respondents allocated100 points over the firms of each industry, which allowedus to measure the loyalty shares among competitors in eachindustry. For example, suppose that a respondent is inter-ested in three supermarkets. For his or her next purchase,he or she allocates 100 points over these three supermar-kets, for example, 40, 30, and 30. These numbers indicatethe probabilities of his or her next visit to these


supermarkets—that is, 40% of the probability to visit thefirst supermarket but only 30% to visit the last two super-markets. This measurement of loyalty intentions reflectsthe increasing polygamous loyalty in customer behaviornowadays (Cooil et al. 2007). The range of loyalty inten-tions was between 0 and 100.

Measurement of CEDs To measure CEDs, we used seven-point Likert scales (1= Btotally disagree^; 7 = Btotally agree^)with multiple items. Web Appendix C provides an overviewof the history of developing measures for CEDs. We limitedthe number of items to increase response rates, as respondentfatigue and lack of time are the main reasons for low responserates (Bergkvist and Rossiter 2007; Böckenholt and Lehmann2015). VE focuses on the price–quality ratio and convenience(Rust et al. 2004; Verhoef et al. 2007); BE measures thebrand’s perceived strength and innovative abilities (Verhoefet al. 2007); and RE consists of items focusing on knowingcustomers’ needs, feeling at home, and feeling commitment(Verhoef 2003; Verhoef et al. 2007). The principal componentanalysis (PCA) indicates that the included measures of CEDsended up in the expected factors (see Web Appendix D).Using Cronbach’s α, we found that the items for VE, BE,and RE are sufficiently reliable, with values of .73, .70, and.85, respectively (see Table 3). We used the averages of theitems of VE, BE, and RE in the data analysis for interpretationpurposes of the interaction effects. We used grand-mean-centered CEDs, instead of group-mean-centered CEDs.Grand-mean-centered CEDs refer to the means obtained fromall responses of the 18 industries. Group-mean-centered CEDsrefer to the means obtained from respondents of each industry.The reason for using grand-mean-centered CEDs is thatmulti-level modeling estimates the mean and variance ofthe parameter values from all industries and firms, ratherthan estimating parameter values for each firm and eachindustry (Hox 2002).4

To examine discriminant validity, we calculated the aver-age variance extracted (AVE) of each CED as well as eachCED’s shared variance with other CEDs (Fornell and Larcker1981). The AVE is .65 for VE, .77 for BE, and .77 for RE; theshared variance is .33 for VE and BE, .30 for VE and RE, and.36 for BE and RE. These results show that the AVE of theCEDs is larger than the shared variance, confirming discrim-inant validity. To test for potential common method bias

(CMB), we used two methods: (1) partial correlation with amarker variable (Lindell and Whitney 2001) and (2) commonmethod factor based on the process in Liang et al. (2007). Forthe first method, Lindell and Whitney (2001) indicate that amarker variable is hardly relevant to the dependent variable.Following Verhoef and Leeflang (2009), we used consumerconfidence as a marker variable. We also used customers’ ageas a marker variable because several empirical studies (e.g.,Bell et al. 2005; Mägi 2003) indicate that age does not have asignificant effect on customer loyalty. By using these twomarker variables, we found that the correlation change of in-dicators and loyalty intentions is small, approximately 1%,after controlling for consumer confidence or age. The secondmethod showed that the substantive factor loading explains 53and 43% of the variance of the indicators before and afterincluding the method factor, respectively. The method factorexplained 23% of the variance. The results of the two methodsindicate that CMB exists in our data, but is not likely a seriousconcern.5

Control variables At the customer level, following Ouet al. (2014), we included theoretically argued variablesinfluencing loyalty intentions: gender, age, income, rela-tionship length,6 involvement, switching costs, andconsumer confidence. At the industry level, we includeddifficulty of evaluating quality prior to consumption as acontrol variable, because Fischer et al. (2010) empiricallyfind that this variable strengthens the relevance of brandsin decision making.

Industry characteristicsWe collected industry characteris-tics mainly from the expert survey, except in the case ofcontractual settings. ICC(2) indicates that the averageagreement rate of experts on industry characteristics was.67 across 18 industries. We employed seven-point Likertscales to assess these characteristics; the relevant questionsappear in Web Appendix D. We used PCA to examinewhether these industry characteristics are unidimensional,as industry characteristics may theoretically correlatewith each other and cause estimation problems dueto multicollinearity (e.g., Evans 1991). For innovative

4 To examine measurement invariance of VE, BE, and RE, we usedstructural equation modeling by comparing every two industries from18 industries, which resulted in 153 comparisons. We randomly chose17 comparisons (approximately 10% of 153) and found that our data donot meet measurement invariance. To account for measurement non-invariance (i.e., cross-industry differences in parameters), Davidov et al.(2012) propose including relevant contextual variables. We thus includedfive industry characteristics and also two firm characteristics as controlvariables in the model.

5 The result of the explained variance (23%) shows that CMB exists but islikely not a concern. Because our respondents are customers from differ-ent firms and different industries, we assume that the explained varianceof CMB comes not only from respondents (i.e., response format) but alsofrom different contexts (Podsakoff et al. 2003). To account for the poten-tial CMB from different contexts, we included five industry and two firmcharacteristics as control variables.6 Of the respondents, 30.6% did not give information about income and8.7% did not know the relationship length. When analyzing the multi-level model, we used (1) the most frequently mentioned value and (2)multiple imputation to replace these missing values. Both methods gavesimilar results. Therefore, we chose the first method to analyze Eq. 1,which we elaborated in the subsequent section.


markets, we originally used eight measures. Three mea-sures ended up with multiple factors, and thus we excludedthem. Web Appendix D shows that the five industry char-acteristics ended up in the expected separate components.The reliability (Cronbach’s α) for the items related to in-dustry characteristics is sufficient, with values between .77and .94 (see Table 3). For each industry, we averaged thefactor scores of each expert’s opinions on the correspond-ing industry characteristics and employed this informationin our data analysis. Finally, with the definition of contrac-tual settings mentioned previously (Fader and Hardie2007), we coded contractual settings as 1 and non-contractual settings as 0.

Firm characteristics We collected firm characteristicsfrom external sources and ACNielsen. We measuredfirms’ market position by their revenue ranking in thecorrespondent industries. We collected revenue informa-tion from firms’ annual reports. We then coded the marketposition by the ascending sequence of firms’ revenues.Namely, we coded firms with the highest revenues in thecorresponding industries as 1 and considered them themarket leader. We coded the remaining firms, which weconsidered market followers, in an ascending sequence

(i.e., 2, 3, 4, and so on). In addition, ACNielsen providesfirms’ annual expenditures in non-price advertising activ-ities. Because we are interested in the relative advertisingexpenditures between competitors in an industry, we di-vided each firm’s advertising expenditures by the totalamount of advertising expenditures in the correspondingindustry.

Model specification

We estimated a multi-level model with four levels to testthe conceptual model, as the original data are hierarchicalwith four levels: customer responses (first level), cus-tomers (second level), firms (third level), and industries(fourth level). We expect that the first level (customer re-sponses) is less straightforward than the other three, andthus we explain why its inclusion is necessary. Some re-spondents in our data repeatedly gave responses to multi-ple firms if they are currently a customer of these firms.Not including this level would produce correlated errorswithin customers and lead to inconsistent estimates. Thisis similar to the idea of repeated measures in the multi-level model (e.g., Hox 2002). We use eijmn in Eq. 2 toaccount for the correlated errors within respondents.

Table 3 Descriptive statistics and correlation matrix

Main variables M SD Samplesize

1 2 3 4 5 6 7 8 9 10 11

Loyalty intentions 42.81 29.76 8924 .30** .34** .39** .13** .003 .01 −.01 .06** −.08** .11** .14**

1. VE 4.98 1.09 8924 .73c .58** .55** .03** −.04** .13** −.01 .09** .10** −.08** −.17**2. BE 4.75 1.09 8924 .70 .60** .11** −.07** .03** −.02+ .08** .04** −.01 −.03**3. RE 4.11 1.24 8924 .85 .04** −.02 .04** −.04** .07** .05** −.02* −.06**4. Advertising expenditures 0.2a 0.16 94d n.a. −.20** −.07** −.06** .07** .21** −.11** −.13**5. Market position n.a. n.a. 75b, d n.a. .13** .23** −.26** −.07** −.03** .04**

6. Competitive intensity 5.59 0.74 18 .89 .44** .03** .06** −.28** −.20**7. Innovative markets 3.86 0.72 18 .85 −.14** −.26** −.01 −.08**8. Complexity of purchase

decisions4.38 0.9 18 .87 .30** −.20** −.13**

9. Visibility to others 3.63 1.10 18 .94 −.30** −.59**10. Difficulty of evaluating

quality4.43 0.63 18 .77 .31**

11. Contractual settings n.a. n.a. 18 n.a.

n.a. not applicable

** P< .01; * p< .05; + p< .1a This is the number of the share of firms’ advertising expenditures = (advertising expenditures of firm i)/(total amount of advertising expenditures of thefirms in one industry)b An ordinal variable. There is no available revenue information for 20 firms in the datac The value of this diagonal is the Cronbach’s αd For data analysis, we created two variables representing the missing value for advertising expenditures and market position (e.g., 1 =missing value;0 = no missing value). We included these two variables in Eq. 1 to retain the same sample size. The dummy variables serve as adjustments for missingvalues


We used a generalized linear model (GLM)7 because itdoes not need to meet the assumptions of normally distributeddependent variables or the homoscedastic variance oferrors required in standard regression models. We usedone of the GLMs, family(binomial) and link(logit). Thefamily(binomial) refers to a binomial distribution of the de-pendent variable. Proportions are assumed to have a binomialdistribution (Baum 2008; Moore and McCabe 2003). Our de-pendent variable, loyalty intentions, is proportional becausethe variable measures self-reported probabilities ofrepurchasing with multiple firms. The link(logit) refers to alogit transformation of the dependent variable, which assumesa linear relationship between this dependent variable and itspredictors. We specify our model as follows:

LIijmn ¼ logit Uijmn

� �: ð1Þ

Level one

Uijmn ¼ π0 jmn þ eijmn: ð2Þ

Level two

π0 jmn ¼ β0mn þ β1mnVEjmn þ β2mnBEjmn

þ β3mnREjmn þ β4mnCVjmn þ η jmn: ð3Þ

Level three

β0mn ¼ γ00n þ γ010 FV0mn þ μ0mn; ð4:1Þ

β1mn ¼ γ10n þ γ110 FV0mn þ μ1mn; ð4:2Þ

β2mn ¼ γ20n þ γ210 FV0mn þ μ2mn; ð4:3Þand

β3mn ¼ γ30n þ γ310 FV0mn þ μ3mn: ð4:4Þ

Level four

γ00n ¼ α000 þ α001IV00n þ ν00n; ð5:1Þ

γ10n ¼ α100 þ α101IV00n þ ν10n; ð5:2Þ

γ20n ¼ α200 þ α201IV00n þ ν20n; ð5:3Þand

γ30n ¼ α300 þ α301IV00n þ ν30n: ð5:4Þ

where

LIijmn loyalty intentions for firm m evaluated bycustomer j in industry n, where i denotes thenumber of firms in industry n that customer jevaluates

logit(Uijmn) the logit link function transforming loyaltyintentions into values on a logit scale rangingfrom –∞ to ∞ (i.e., Uijmn)

VEjmn VE for firm m evaluated by customer j inindustry n

BEjmn BE for firm m evaluated by customer j inindustry n

REjmn RE for firm m evaluated by customer j inindustry n

CVjmn customer-level control variables—a row vectorof gender, age, income, relationship length,switching costs, involvement, and consumerconfidence

FV0mn firm characteristics—a row vector of marketposition and advertising expenditures

IV00n industry characteristics—a row vector ofcompetitive intensity, innovative markets,contractual settings, visibility to others,complexity of purchase decisions, anddifficulty of evaluating quality prior toconsumption (as a control)

eijmn level-one residuals (repeated level)ηjmn level-two residuals (customer level)μymn level-three residuals (firm level), y=0,1, 2, 3;

andνz0n level-four residuals (industry level), z= 0, 1,

2, 3.

In addition, π0jmn is the random level-one intercept;β0mn isthe level-two intercept;β1mn,β2mn, andβ3mn are the effects ofVE, BE, and RE, respectively; β4mn is a vector of coefficientscorresponding to customer-level control variables; γ00n, γ10n,γ20n, and γ30n are level-three intercepts; γ010 is a vector of theeffects of firm characteristics on loyalty intentions; γ110, γ210,and γ310 are a vector of coefficients for the interaction terms atthe firm level; α000, α100, α200, and α300 are level-four inter-cepts; α001 is a vector of the effects of industry characteristicson loyalty intentions; α101, α201, and α301 are a vector ofcoefficients for the interaction terms at the industry level.

Empirical results

Model fit

Table 4 contains the parameter estimates of three differentmulti-level models. We mean-centered all the variables in

7 Web Appendix E presents the tests of these assumptions. We used thegllamm in Stata 12 for the GLM model.


Table 4 Multi-level modelresults for loyalty intentions Model 1 Model 2 Model 3

Coeff. S.E. Coeff. S.E. Coeff. S.E.VE 1.86** .14 1.84** .19 1.95** .20BE .66** .12 .49** .14 .56** .18RE 1.79** .11 1.69** .13 1.07** .16

Industry-level moderators

VE × competitive intensity (H1ve: −) −1.62** .44 −1.02** .33

BE × competitive intensity (exploration) .06 .34 .32 .27

RE × competitive intensity (exploration) .12 .26 −.10 .25

VE × innovative markets (H2ve: +) .74** .28 .33 .30

BE × innovative markets (H2be: +) .91** .33 .67* .28

RE × innovative markets (exploration) −.45* .23 −.73** .21

VE × contractual settings (exploration) .22 .41 .35 .40

BE × contractual settings (exploration) .34 .38 .11 .36

RE × contractual settings (H3re: +) .99** .28 .70* .31

VE × visibility to others (H4ve:+) .80* .38 1.88** .38

BE × visibility to others (H4be:+) .35 .35 −.34 .36

RE × visibility to others (H4re: −) −1.45** .31 −1.57** .31

VE × complexity of purchase decisions(H5ve: +)

−.68** .19 −.40* .20

BE × complexity of purchase decisions(H5be: +)

.31+ .18 .44* .19

RE × complexity of purchase decisions(H5re: +)

.55** .15 −.08 .17

VE × difficulty of evaluating quality prior toconsumption (control)

−.18 .33 .11 .28

BE × difficulty of evaluating quality prior toconsumption (control)

.21 .27 .33 .27

RE × difficulty of evaluating quality prior toconsumption (control)

−.92** .22 −.89** .25

Firm-level moderators

VE × market position (exploration) .14+ .08 .12 .08

BE × market position (exploration) −.08 .08 .04 .08

RE × market position (exploration) .21** .07 .07 .07

VE × advertising expenditures (H6ve: −) −1.59 1.01 −4.64** .96

BE × advertising expenditures (H6be: +) .91 1.02 2.30* 1.05

RE × advertising expenditures (exploration) .59 .90 −1.59+ .89

Customer-level moderators

VE × female (1, vs. male: 0) .72* .28

VE × age .13 .12

VE × income −.09 .16

VE × relationship length .20* .08

VE × switching costs .08 .08

VE × involvement .01 .10

VE × consumer confidence −.78** .15

BE × female (1, vs. male: 0) −.23 .26

BE × age −.42** .12

BE × income −.08 .14

BE × relationship length .04 .07

BE × switching costs −.05 .07

BE × involvement .30** .11

BE × consumer confidence .12 .14

RE × female (1, vs. male: 0) 1.37** .25

RE × age .30** .10

RE × income .59** .12


the model to derive interpretable coefficients (Hox 2002).Model 1 includes the main effects of CEDs, controlling forthe industry-, firm-, and customer-level variables. To ex-plain the variance of the effects of CEDs on loyalty inten-tions, Model 2 includes the industry- and firm-level mod-erators. Model 3 estimates the joint moderating effects ofindustry, firm, and customer characteristics. We focus onModel 3 when discussing the results of the moderatingeffects because it has the best model fit (−2631.04;Akaike information criterion = 5390.08) and is significant-ly better than Model 1 (χ2 =−2631.04 – (−2757.04) = 126,df= 45, p< .01).

Main effects of CEDs

Consistent with previous research (Rust et al. 2004; Vogelet al. 2008), we found a positive link between CEDs and

loyalty intentions (VE = 1.95, p < .01; BE = .56, p < .01;RE=1.07, p< .01). This finding provides an additional empir-ical generalization of the positive CEDs–loyalty link acrossvarious industries and firms (see also Ou et al. 2014).

Moderating effects of industry characteristics

Competitive intensityAs expected, competitive intensity hada significant, negative interaction with VE; that is, the effect ofVE is weaker in competitive industries (−1.02, p < .01).However, through exploration, we found that competitive in-tensity had no moderating impact on the effect of BE (.32,p> .1) or RE (−.10, p> .1). Thus, H1ve is supported.

Innovative markets As expected, we found a moderatingeffect of innovative markets on the BE–loyalty link. This linkis strengthened in innovative markets (.67, p< .05). However,

Table 4 (continued)Model 1 Model 2 Model 3

Coeff. S.E. Coeff. S.E. Coeff. S.E.VE 1.86** .14 1.84** .19 1.95** .20BE .66** .12 .49** .14 .56** .18RE 1.79** .11 1.69** .13 1.07** .16

RE × relationship length −.38** .07

RE × switching costs −.05 .06

RE × involvement −.08 .09

RE × consumer confidence −.24* .12

Customer-level drivers

Female (1, vs. male: 0) −.17 .20 −.07 .20 .40 .26

Age .15+ .09 .13 .09 .16 .11

Income −.05 .11 −.00 .11 .17 .14

Relationship length (RL) .14* .06 .07 .06 .02 .08

Switching costs (SC) −.22** .07 −.08 .07 −.13+ .07

Involvement −.61** .08 −.50** .09 −.37** .11

Consumer confidence (CC) −.45** .12 −.34*** .13 −.89** .17

Industry-level drivers

Competition intensity 2.73** .39 3.73** .42 1.32** .43

Innovative markets −2.61** .26 −2.77** .34 −1.37** .31

Contractual settings −1.40** .48 −.67 .55 −1.64** .59

Visibility to others −.53 .36 −.45 .50 .48 .47

Complexity of purchase decisions 1.53** .17 1.78** .23 1.67** .25

Difficulty of evaluating quality prior toconsumption

.25 .43 −3.19** .45 −1.94** .60

Firm-level drivers

Market position −.13 .08 −.20* .08 −.55** .09

Advertising expenditures 3.65** 1.0 −.16 1.10 −3.43** 1.22

Intercept 13.10** .60 10.92+ 5.96 23.34** 1.02

Log-likelihood −2757.04 −2684.76 −2631.04Akaike information criterion 5552.08 5455.52 5390.08

R2 .26 .28 .28

** P< .01; * p< .05; + p< .1


we did not find a significant interaction between innovativemarkets and VE (.33, p> .1). These results provide support forH2be but not for H2ve. Through exploration, we found thatinnovative markets decrease the effect of RE (−.73, p< .01).

Contractual settingsAs expected, the effect of REwas stron-ger in contractual settings (.70, p< .05), in support of H3re.However, our results indicate that contractual settings do notinfluence the effects of VE (.35, p> .1) and BE (.11, p> .1) onloyalty intentions.

Visibility to others As expected, when the usage of goods/services is visible to others, the effect of VE is stronger (1.88,p < .01), but the effect of RE (−1.57, p< .01) is weaker.Therefore, H4ve and H4re are supported. However, the resultsshow no support for H4be, as we did not find a significantinteraction between BE and visibility to others (−.34, p> .1).

Complexity of purchase decisions We expected that the ef-fects of CEDs would be stronger when customers encountercomplexity in purchase decisions. In such cases, however, wefound that the effect of VE decreases (−.40, p< .05), while theeffect of BE (.44, p< .05) increases. The relationship betweenRE and loyalty intentions was not affected (−.08, p> .1). Thus,H5be is supported, while H5ve and H5re are not supported.

Moderating effects of firm characteristics

Market position Double jeopardy theory and informationaccessibility theory expect market position to have opposing

moderating effects on the CEDs–loyalty link. Through explo-ration, we did not find a moderating impact of firms’ marketposition on the link between loyalty intentions and any of thethree CEDs (VE: .12, p> .1; BE: .04, p> .1; RE: .07; p> .1).

Advertising expenditures We found that firms’ advertisingexpenditures have a moderating impact on the effects of allthree CEDs. As expected, the effect of VE (−4.64, p< .01) isweaker, but the effect of BE (2.30, p< .05) is stronger for firmsthat invest heavily in advertising. Thus, H6ve and H6be aresupported. We also found that the RE×advertising interactionis marginally significant (−1.59, p< .1).

Explained variance of the effects of CEDs at the industryand firm level

We calculated the extent to which the cross-industry andcross-firm variance of the effects of CEDs is explained byincluding five industry and two firm characteristics. Table 5reports the results. Model 1 shows that the cross-industry var-iances of VE (1.66, p< .01), BE (1.48, p< .01), and RE (2.45,p< .01) are significant. The cross-firm variances of VE (1.21,p< .01), BE (1.86, p< .01), and RE (1.34, p< .01) are alsosignificant. The significant variance indicates that the effectsof CEDs differ substantially across industries and firms.Therefore, it is important to explain why such a variance takesplace. After we included industry and firm characteristics inModel 2, the cross-industry variance of CEDs decreases andbecomes non- significant (.07 for VE, .39 for BE, and .53 forRE; p> .1). The cross-firm variance of CEDs also decreases.

Table 5 (Explained) variance ofthe effects of CEDs acrossindustries and firms

Model 1 Model 2 Model 3

Coeff. S.E. Coeff. S.E. Coeff. S.E.

(Explained) variance of CEDs effects across industries

VE 1.66** .43 .07 .30 .02 .29

Explained variance of VE effecta 95.8% 98.7%

BE 1.48** .36 .39 .28 .34 .22

Explained variance of BE effecta 73.6% 77.0%

RE 2.45** .49 .53 .43 .19 .15

Explained variance of RE effecta 78.4% 92.2%

(Explained) variance of CEDs effects across firms

VE 1.21** .46 .97* .47 .87+ .51

Explained variance of VE effecta 19.8% 28.1%

BE 1.86** .35 .65 .67 .57 .76

Explained variance of BE effecta 65.1% 69.4%

RE 1.34** .36 1.29** .34 1.13** .36

Explained variance of RE effecta 3.7% 15.7%

** P< .01; * p< .05; + p< .1a The percentage refers to the extent to which the variance of the CEDs effects in Model 1 is explained. Forinstance, the explained cross-industry variance of the VE effect inModel 3 = {(1.66–0.02)/1.66} × 100%= 98.7%


The variance of BE (.65, p> .1) becomes non-significant, butthe variance of VE (.97, p< .05) and RE (1.29, p< .01) re-mains significant. Finally, Model 3 shows that the cross-industry variance is explained by 98.7% for VE, 77.0% forBE, and 92.2% for RE. The cross-firm variance is explainedby 28.1% for VE, 69.4% for BE, and 15.7% for RE. Most ofthe cross-industry variance is explained, but the cross-firmvariance is moderately explained.

Robustness checks

To test whether the results of Model 3 in Table 4 are robust(i.e., the 11 significant or marginally significant interactions ofCEDs and industry- and firm-level characteristics), we con-ducted four robustness checks: (1) examining an alternativemodel with link(probit), (2) excluding commitment in theconstruct of RE because some argue that commitment is adimension of loyalty, rather than a dimension of RE (e.g.,Morgan and Hunt 1994), (3) using a smaller dataset (exclud-ing one-third of the total sample) to examine coefficient reli-ability, and (4) including the interactions of CEDs. Most re-sults of the robustness checks are consistent with those ofModel 3. We reported the results of the robustness checks inWeb Appendix F.

Discussion

Summary of findings

This study extends prior research on the differential effective-ness of loyalty strategies across industries and firms by sys-tematically developing a framework of industry and firm char-acteristics as moderators. We used multiple sources of data toempirically examine how these contextual characteristics in-fluence the role of CEDs in loyalty decisions. We also

controlled for customer-level moderators. Our findings, sum-marized in Table 6, give a better understanding of (1) whichcontextual characteristics actually have a contingent impact onthe link between CEDs and loyalty intentions and (2) whichspecific contexts render CEDs as more or less effective. Asspeculated in prior literature, most of the identifiedmoderators(except market position) indeed have a contingent impact.Advertising expenditures constitute the most crucial modera-tor because they significantly influence VE and BE and mar-ginally significantly influence RE. Three industry contexts(i.e., innovative markets, visibility to others, and complexityof purchase decisions) each influence two CEDs. Two indus-try contexts (i.e., competitive intensity and contractual set-tings) each influence one CED. However, the data do notprovide support for the following contingent hypotheses: (1)innovative markets for the VE–loyalty link, (2) visibility toothers for the BE–loyalty link, and (3) complexity of purchasedecisions for the VE–loyalty and RE–loyalty links. In thefollowing discussion, we provide potential explanations forthese unsupported findings. We explain only the hypothesesfor which the data provide no support. We do not explain theunfound exploration effects.

Unsupported findings

Innovative markets: VE We expected that VE would beimportant in innovative markets because firms in these mar-kets aim to improve VE (Hauser et al. 2006), which should bean important criterion for customers when deciding to repur-chase. However, we find that innovative markets do not exerta moderating effect on the link between VE and loyalty inten-tions. While we expect a positive moderating effect of inno-vative markets on VE, it might also be cancelled out by apotential negative effect. A potential reason is that firms ininnovative markets commonly aim to improve VE, renderingthe fulfillment of VE a basic requirement for firms trying to

Table 6 Summary of the resultsVE BE RE

Hypoth. Result Hypoth. Result Hypoth. Result

Industry characteristics

Competitive intensity H1ve (−) − Explore 0 Explore 0

Innovative markets H2ve (+) 0 H2be (+) + Explore −Contractual settings Explore 0 Explore 0 H3re (+) +

Visibility to others H4ve (+) + H4be (+) 0 H4re (−) −Complexity of purchase decisions H5ve (+) − H5be (+) + H5re (+) +

Firm characteristics

Market position Explore 0 Explore 0 Explore 0

Advertising expenditures H6ve (−) − H6be (+) + Explore −a

0: no effectaMarginally significant (p< .1)


survive in such markets (Fredericks and Salter 1995; Rustet al. 2000). This may indicate that customers perceive fewerdifferences in VE among firms in innovative markets and thusare less likely to employ VE as a comparison standard fordifferentiating firms in such markets (Slater 1997). In anycase, additional research is required to understand the moder-ating role of innovative markets.

Visibility to others: BE Prior studies have widely assumedthat BE is important in visible goods/services because it ismore publicly noticeable than VE and RE (e.g., Fischeret al. 2010; Rust et al. 2000). However, our data do not con-firm this expectation, implying that BE is not particularly im-portant for respondents in visible goods/services. A potentialexplanation is the relative importance of signal credibility orself-identity—the two functions of BE—in customers’ loyaltydecisions (Erdem et al. 2006; Rust et al. 2000). We assumethat customers focusing on the function of signal credibilitymay ignore the importance of BE being publicly noticeable, assignal credibility is more about evaluating the attributes ofgoods/services and reducing performance risk. In contrast,customers focusing on the function of self-identity may em-phasize the importance of BE being publicly noticeable, as therelevance of BE in decision making is greater for social de-monstrativeness and the need to make a good impression(Bearden and Rose 1990; Fischer et al. 2010). Further researchcould distinguish between the self-identity and risk-reducingroles of brands. In our limited measurement of BE, we did notmake such a distinction.

Complexity of purchase decisions: VE and REWe hypoth-esized that complexity of the purchase decision wouldstrengthen the effects of all CEDs. However, the findings weremixed.We found only a significant, positive moderating effecton the BE–loyalty link. Conversely, the effect of VE on loy-alty intentions became weaker in the face of complexity, andthis variable failed to show a moderating effect on the RE–loyalty link. The reasoning for these mixed results may derivefrom customers perceiving different sources of purchase deci-sion complexity: the extensive amount of information re-quired for choosing goods/services, on the one hand (Rustet al. 2000), and choices among too many competitors, onthe other hand (Scheibehenne et al. 2010). These two sourcesimply a positive and negative moderating impact of purchasedecision complexity, respectively. Our argument for the hy-potheses derives from the first source (positive moderation),as research suggests that CEDs are crucial factors in reducingthe complexity of purchase decisions (Rust et al. 2004; Vogelet al. 2008). Regarding the second source (negative modera-tion), customers tend to experience choice overload whenconfronted with too many competitors, which leads to diffi-culty in justifying preferences, less motivation to choose, andless ability to make optimal choices (Scheibehenne et al.

2010). As such, customers may be less able to accuratelytransfer their perceptions of firms into loyalty intentions. Ifso, the CEDs–loyalty link becomes weaker for customers withchoice overload. Thus, to further validate the moderating roleof purchase decision complexity on the CEDs–loyalty link,research should distinguish between the sources of complexityperceived by customers.

Managerial implications

Because resources are limited, Bit is essential that firms iden-tify their industry’s success factors, paying more attention tothe [CEDs] that drive customer choice, and perhaps payingless attention to the ones that don’t^ (Rust et al. 2000, p. 262).To achieve effective resource allocation, managers should de-fine success factors in their own specific context.

Figure 2 provides managers across different services indus-tries with information about the industry and firm characteris-tics that actually influence the effectiveness of CEDs andwhichCEDs are most and least effective in their own specific context.For example, Panel A shows that changes in loyalty intentionsare 24.1, 6.9, and 15%when firms are able to increase VE, BE,and RE, respectively, by one standard deviation.8 However, ourfindings indicate that not all industries and firms uniformlybenefit from these numbers. Without taking the context intoaccount, managers may be mistaken about the idiosyncraticeffects of CEDs in specific contexts. Panels B, C, and D displaythe idiosyncratic effects of VE, BE, and RE across differentindustries and firms, respectively. We use Panel D (i.e., RE)as an illustrative example.

Panel D offers two insights into the idiosyncratic effects ofRE across industries and firms. First, the following industryand firm characteristics do not influence the effectiveness ofRE as changes in loyalty intentions in these characteristicsremain at 15% (see Panel A): (1) competitive intensity, (2)complex purchase decisions, and (3) market position. In termsof contextual impact, these characteristics play a less impor-tant role in the effectiveness of RE strategies. Second, RE ismore effective in contractual settings, as change in loyaltyintentions rises from 15 to 24.8%. However, RE is less effec-tive in the following contexts as changes in loyalty intentionsacross all the industries are lower than 15%: (1) innovativemarkets, (2) industries whose products are more visible to

8 We follow the idea of Luo and Bhattacharya (2009) and visualize theextent to which loyalty intentions change by implementing CEDs strate-gies. Take VE, for example. We calculated changes in loyalty intentionswhen increasing VE by one standard deviation as follows:

β1mn � oneSDof VEvarianceof loyalty intentions

¼ 1:95� 1:09

8:85¼ 0:241;

where β1mnis from Eq. 3 of the model specification and SD refers tothe standard deviation of VE. Tomatch the seven-point scale of CEDs, wedivided loyalty intentions (0–100) by 10 to get into the range between 0and 10. Thus, the variance of loyalty intentions is (2.976)2 = 8.85.


others, (3) industries in which customers have trouble evalu-ating quality, and (4) heavy advertisers. These results implythat similar RE strategies indeed create different levels of loy-alty intentions in different industries and different firms.

Therefore, to effectively allocate resources, managersshould not only consider the effectiveness of loyalty activitiesbut also align those activities with the influential idiosyncra-sies that define their firms’ contexts. Ignoring these contextsmay result in a sub-optimal allocation of resources and a fail-ure to reach desired performance.

Limitations and further research

As with other empirical studies, our study has some limita-tions that provide avenues for further research. First, becausewe aimed to extend the Rust et al. (2000) model, we followedtheir method of using loyalty intentions, rather than behavioral

loyalty, as the dependent variable. In fairness, it is not current-ly feasible to compare behavioral loyalty across industries fora large number of firms, as behavioral loyalty has differentmeanings across industries. However, we recognize that actu-al, observed loyalty behavior is the ultimate proof of loyaltyand is more related to the metrics of firm performance (e.g.,De Haan et al. 2015). In addition, although CMB is not aserious concern in our data, it does exist, and one remedyfor CMB entails using behavioral loyalty. We thus encouragefurther research to uncover an adequate proxy of behavioralloyalty as a comparison criterion across industries.

Second, the large-scale dataset limits the number of itemswe could use for CEDs. Although Cronbach’s α shows suffi-cient reliability for CEDs, we admit that more items per con-struct could have been used. For example, BE can be mea-sured not only by being strong and innovative but also bybeing liked (e.g., Vogel et al. 2008). In addition, the

24.1%

6.9%15%

VE BE RE

14.8%

24.1% 24.1%

49.6%

19.7%24.1% 24.1%

15%

Highlycompetitive

More/ lessinnovative

markets

(Non)contractual

settings

Morevisibility to

others

Morecomplexity

More/ lessdifficulty

Marketleaders/

followers

Heavyadvertisers

Industry characteristics Firm characteristics

6.9%12.8%

6.9% 6.9%11.8%

6.9% 6.9%11.4%

Highly/ lesscompetitive

Innovativemarkets

(Non)contractual

settings

More/ lessvisibility to

others

Morecomplexity

More/ lessdifficulty

Marketleaders/

followers

Heavyadvertisers


15%

7.6%

24.8%

0%

15%

7.1%

15%11.4%

Highly/ lesscompetitive

Innovativemarkets

Contractualsettings

Morevisibility to

others

More/ lesscomplexity

Moredifficulty

Marketleaders/

followers

Heavyadvertisers


B: Changes in loyalty intentions by VE across industries and firms

C: Changes in loyalty intentions by BE across industries and firms

D: Changes in loyalty intentions by RE across industries and firms

A: Changes in loyalty intentions without the impact of industry and firm characteristics Fig. 2 Changes in loyaltyintentions by implementing CEDs


measurements of CEDs are non-invariant across industries.Although we included five industry and two firm characteris-tics to account for measurement non-invariance (Davidovet al. 2012), we understand that it cannot be completelyaccounted for.

Third, we incorporated five rarely examined industry-levelmoderators in the customer equity model, as well as exploredtwo firm-level moderators. This is an initial step, but addition-al industry and firm characteristics are required. For example,in cases in which market-oriented firms have invested a greatdeal in building relationships, will these firms benefit morefrom continuing their investments in CRM or from investingin alternative loyalty strategies, such as VE or BE? Anotherexample we did not consider is the servicescape, which is afirm-level characteristic involving the physical environment inwhich goods or services are purchased.

Fourth, with regard to the scope of the research setting, thedataset is limited to business-to-consumer firms in servicesindustries in the Netherlands. To further generalize our find-ings, researchers could test whether the uncovered moderatingrole of industry and firm characteristics emerge in other indus-tries (e.g., B2B) and other countries. For example, someresearchers argue that B2B customers tend to focus on part-nership cooperation (e.g., Palmatier et al. 2006). This argu-ment implies that the contextual contingencies may have lessimpact on the link between RE and loyalty intentions.

Lastly, our large-scale dataset is limited to cross-sectionalvariation and cannot examine changes of the moderating roleof industry and firm characteristics over time. Given that thetrend of the market environment is toward more intense com-petition and R&D investment, it is important to knowwhetherthese industry characteristics gain more impact over time.

Acknowledgments The authors thank the Customer Insights Center(specifically the managing director Jelle Bouma) of the University ofGroningen for supporting this research. They thank Metrixlab,MICompany, and ACNielsen for research and data-support. They alsothank three anonymous JAMS reviewers and the AE for their insightful,helpful comments and suggestions. We also thank the current editorRobert W. Palmatier and the prior editor Tomas Hult for their supportand comments.

Open Access This article is distributed under the terms of the CreativeCommons At t r ibut ion 4 .0 In te rna t ional License (h t tp : / /creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided you giveappropriate credit to the original author(s) and the source, provide a linkto the Creative Commons license, and indicate if changes were made.

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